US20070009155A1 - Intelligent importation of information from foreign application user interface using artificial intelligence - Google Patents
Intelligent importation of information from foreign application user interface using artificial intelligence Download PDFInfo
- Publication number
- US20070009155A1 US20070009155A1 US11/358,436 US35843606A US2007009155A1 US 20070009155 A1 US20070009155 A1 US 20070009155A1 US 35843606 A US35843606 A US 35843606A US 2007009155 A1 US2007009155 A1 US 2007009155A1
- Authority
- US
- United States
- Prior art keywords
- representation
- selected data
- data
- raster
- graphic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013473 artificial intelligence Methods 0.000 title description 5
- 238000013528 artificial neural network Methods 0.000 claims abstract description 89
- 238000000034 method Methods 0.000 claims abstract description 83
- 239000013598 vector Substances 0.000 claims abstract description 34
- 238000012545 processing Methods 0.000 claims description 35
- 238000009877 rendering Methods 0.000 claims description 11
- 230000003993 interaction Effects 0.000 claims 4
- 230000011218 segmentation Effects 0.000 abstract description 14
- 238000000605 extraction Methods 0.000 abstract description 3
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 17
- 238000012549 training Methods 0.000 description 17
- 238000007781 pre-processing Methods 0.000 description 13
- 238000012805 post-processing Methods 0.000 description 11
- 238000012360 testing method Methods 0.000 description 10
- 239000000306 component Substances 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- 238000003672 processing method Methods 0.000 description 7
- 239000003086 colorant Substances 0.000 description 5
- 210000002569 neuron Anatomy 0.000 description 5
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 230000003213 activating effect Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 240000005020 Acaciella glauca Species 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013398 bayesian method Methods 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000001143 conditioned effect Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010422 painting Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000000135 prohibitive effect Effects 0.000 description 1
- 235000003499 redwood Nutrition 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
Abstract
Description
- This application claims priority under 35 U.S.C. 119(e) and 37 C.F.R. 1.78(a)(4) based upon copending U.S. Provisional Application, Ser. No. 60/655,065 for INTELLIGENT IMPORTATION OF INFORMATION FROM FOREIGN APPLICATION USER INTERFACE BY LAYERED APPLICATION UTILIZING ARTIFICIAL INTELLIGENCE filed Feb. 22, 2005.
- The present invention relates to a process for integrating document management and imaging system functionality with a user interface of a “foreign” or unrelated application in a separate process through the use of artificial neural networks.
- All forms of business require record keeping related to employees, customers, suppliers, service providers, inventory, transactions, billing, payments, and the like. For such record keeping, most companies employ business application software which typically includes database management functions along with calendar and financial functions. In addition to traditional types of database records, it is also necessary to keep records of supporting documents, such as orders, invoices, statements, checks, photographs, diagrams, bids, contracts, disclaimers, and so forth. Many existing business applications have no built-in capability for accessing or managing digitized copies of such supporting documents, with the necessity that paper copies be stored. It is generally not desirable to modify an existing business application to add image management capabilities and may also be contrary to the terms of the license for the software. Alternatively, the costs and disruptions involved in installing new software with built-in image management capabilities and training employees to use it can be prohibitive. For these reasons, techniques to “image enable” existing business applications have been developed.
- Current desktop computers and operating systems have the capability of multitasking, that is, executing multiple programs simultaneously or, at least, having multiple programs open simultaneously and quickly accessible. Thus, it is possible to run a business application and an image management program simultaneously on commonly available computers. The business application is used to process business records while the image management program provides access to stored digital images of documents which support the business records. The goal of image enablement is to provide a convenient link between business records and the particular image files related to those records.
- The linking of elements of business records to images often involves keying the contents of a particular data field of a business record to the file name of the digital image. For example, a customer account number, an invoice number, an address or phone number, a social security number, or the like which appears in an appropriate data field of a record could be used as a key to an associated image. The contents of the data field is selected automatically, by the image management software placing a cursor at the beginning of the character string or by highlighting the string. The image management software is activated by either a particular keystroke combination or a mouse click on an icon on a toolbar or task bar, and a scanned image of a supporting document is saved along with the contents of the data field, and possibly other information, such as the position of the data field on the screen. The linking software obtains the data field contents by reading it from the display buffer or by intercepting the data as it is output to the computer display. The image of the supporting document can be recalled when the associated record is displayed by selecting the variable of the appropriate data field and activating the image management software which then retrieves the image file with which the data field variable is associated and displays the image. A process for such an image enablement technique is detailed in U.S. Pat. No. 5,586,240, which is incorporated herein by reference.
- The image enablement processes described above are suitable for business applications which are text based since text characters are typically encoded in a standard manner, such as in ASCII codes. However, some applications do not output display data in a text format with some graphics, but in an entirely graphics format. This is sometimes referred to as “painting the screen” with rasterized or pixel format data which depicts text in any of a number of fonts of selected point sizes and colors along with various kinds of graphics. Business applications with such displays are not suited to conventional processes for image enablement.
- The present invention provides a greatly improved means for translating information from a host application into data that can be stored to link images or documents between two different applications. The techniques embodying the present invention can be applied to a host application to enable external images and documents to be associated with data in the host application which is displayed in a non-ASCII, rasterized text format and without affecting operation of the host application.
- The present invention relates to a method for the acquisition of data across process boundaries on a computer system. The present invention is directed to an intelligent importation process for extracting selected data from an application program which displays the data as a raster representation thereof, without affecting operation of the application program, and converts the raster data to another form, such as ASCII text data, using one or more artificial neural networks.
- The intelligent importation process is set up for a particular application by selection of one or more areas of a screen displayed by the application, such as a data record, from which data will be extracted. The areas correspond, for example, to data fields of such a data record and will contain text data which can be used as keys associated with files external to the host application, such as images in an image management system. Coordinates of the selected screen areas are stored by the process. The text data which populates actual data records will be displayed in a rasterized or pixel format. The intelligent importation process is trained to separate or segment characters displayed in the rasterized text data and recognize the characters using one or more artificial neural networks. Once the process has been trained and set up, it can be used to perform operations based on the extracted and recognized rasterized data displayed by the host application, without affecting operation of the application. In the present invention, the process is used to “image enable” the host application by enabling the storage and retrieval of images associated with particular data records. While the present invention is primarily described in terms of associating keys with image files, it is recognized that other types of files external to the host application could be keyed for association with data records, such as sound files, video clips, and even external text files. Such types of data files are considered “external” to the host application in that the host application is not capable of directly rendering data from such files, that is, displaying or playing data from such external data files. Such other types of data files are considered to be functionally equivalent to image files and are intended to be encompassed by the present invention.
- In general, the invention can be divided into three processing methods and three phases that each utilize the three methods. The three processing methods include raster processing, feature processing, and vector processing. The three phases include a training phase, a testing phase, and a production phase. With the use of artificial intelligence algorithms and data constructs, data is extracted from an application or program based on the raster representation displayed by that application. The use of artificial intelligence and the means by which the raster representation is both pre-processed and post-processed are integral parts of the present invention.
- In a training phase of the present invention, the artificial neural networks are conditioned to recognize different input sets as the same data value, and the raster input is processed to morph it into an input set that can be worked on by the different neural networks. The training phase is made up of many executions of the training logic including the knowledge of the data each raster region represents. Based on the scores that the artificial neural networks generate for each possible data output, the decision points in the network are adjusted to move the network closer to the correct answer. This process is called “feed-forward” and “back-propagation”.
- The pre-processing of the raster representation of the image begins with isolating the area to be recognized by the artificial neural networks. In this case that will be one data item at a time. A typical data item is a pixel or raster representation of an alphanumeric character, although other symbols could also be employed, such as punctuation symbols, non-English characters, and the like. Once the region has been separated it is then run through a thresholding algorithm that separates the pixels of interest from background noise in the application. The result of this step is a binary raster representation of the region to be recognized. Following the threshold step a segmentation loop is initiated. In the training phase, this processing is limited since only a single data item, of a training set of items, is drawn at a time to train the artificial neural networks.
- At this point the segmented raster representation is saved, and a copy is made to enable processing the raster data in different ways to provide varying inputs to different artificial neural networks associated with the three processing methods. The first network is a raster artificial neural network. To morph the raster data to meet these inputs, it must be scaled using bi-cubic interpolation to a common size. That is, the pixel grid of the raster data of interest must be dimensionally scaled to a standard size using so-called bi-cubic interpolation, which is a common technique for enlarging and reducing the size of digital images. Two standard sizes that are preferred in the present invention are 16×16 and 12×12 pixels, corresponding to networks of 256 inputs and 144 inputs respectively to the raster processing artificial neural network. These are just two of the possible embodiments of the input to the raster artificial neural network and are not intended to limit the scope of the invention as defined in the appended claims. While performing the bi-cubic interpolation, the binary raster data is transformed into 256 level grayscale data. That data is then transformed between −1 and 1 where a 0 grayscale value will equal −1 and a 256 grayscale value will equal 1.
- Then each data item is processed and the training logic is performed, as well as the feed-forward and back-propagation. In this process the rate at which the decision points or neuron weights are altered is adjusted as the process gets close to the local minima which will capture the best approximation of the recognition function across the input set. Should the mean squared error increase, the amount by which the decision points or neuron weights are altered is reduced. Conversely, should the mean squared error decrease, the amount by which the decision points or neuron weights on each back-propagation are adjusted is increased. This network can be trained independent of the other network types employed.
- A copy of the segmented raster representation of the data item is used for feature based processing which is the second processing method. This time the data will be transformed for input into a feature based artificial neural network. Additional processing is done prior to extracting these features. First, the image is scaled to a 50 by 50 pixel raster representation and skeletonized, and a staircase removal algorithm is employed to scrub the pattern down to a consistent representation that is font independent and can be analyzed for such character “features” as vertical and horizontal histograms, endpoint locations, and intersection locations (based on a 9×9 image grid to limit the inputs). In the same manner as in the raster network, these features are translated into inputs to an artificial neural network that is being trained to recognize the characters in the same feed-forward, back-propagation method as the raster case.
- The third processing method is vector based processing which begins with a copy of the segmented raster representation of the data item. This time the data will be transformed into a vector representation and the bounding sine wave will be calculated using a Fourier Transform. Generally, a rough outline of a character is made of a sequence of vectors having run-length magnitudes and directions of segments of the outline, for example 30 such segments. The first 30 Fourier coefficients will be processed to be translation, rotation, and scale invariant and then used as inputs for the vector artificial neural network recognition system. These inputs are then fed into an artificial neural network that is being trained to recognize the characters in the same feed-forward, back-propagation method as both the raster and the feature based cases.
- Once the training phase has been completed (that is each of the above engines correctly identify the training set and have an acceptable mean squared error), each engine is then run through a testing set of data items which are not included in the training set. Based on the recognition rates and the success rates for certain characters a voting structure is set up which receives the outputs of the raster, feature, and vector processes.
- To extract data from another process in this manner, the screen is learned. The user must identify the application that data is to be extracted from as well as the areas where the data can be found in the application. Once the region on the application is isolated, recognition processes can be initiated. The raster representation of this region is extracted from the application and run through a thresholding method to remove extraneous pixels, such as boxes around the region, and transform it into a binary raster representation. Then a recognition loop is initiated. Typically, the raster representation selected contains a string of data items or characters to be recognized and associated with an image or document external to the host application. The first data item is segmented at the most likely point according to a vertical histogram of the entire region, and that raster representation is processed through instances of the artificial neural networks identified in the training phase and the methods outlined for recognition in each one. The output vectors of the different networks are then compared based on manual configuration settings and a vote is taken for the most likely character. If the voting confidence falls below a predefined level the raster representation is placed back into the region and a new segmentation point is taken based on the vertical histogram and the process is repeated until the maximum possible raster ratio for a data element is exceeded or an acceptable recognition takes place. This phase uses all the pre-processing, recognition and post-processing steps to translate the raster representation of an application into data used by a document management system.
- Objects and advantages of this invention will become apparent from the following description taken in conjunction with the accompanying drawings wherein are set forth, by way of illustration and example, certain embodiments of this invention.
- The drawings constitute a part of this specification and include exemplary embodiments of the present invention and illustrate various objects and features thereof.
-
FIG. 1 is a simplified block diagram illustrating principal components of an environment of operation of a process for intelligent importation of information from a foreign application user interface which embodies the present invention. -
FIG. 2 is a simplified flow diagram illustrating principal functional operations of the intelligent importation system of the present invention. -
FIG. 3 is a block diagram illustrating the principal functional components of the intelligent importation process of the present invention. -
FIGS. 4 a and 4 b are a flow diagram illustrating principal steps in the process of the present invention. -
FIG. 5 is a flow diagram illustrating post-processing steps in the process of the present invention. -
FIG. 6 is a raster diagram of an upper case “i” shown in a 10 by 10 pixel matrix which can be applied as inputs to an artificial neural network for training or recognition in the present invention. -
FIG. 7 is a diagram of an upper case “i” for processing using a Fourier transform to generate coefficients which will be applied to an artificial neural network for training or recognition in the present invention. -
FIG. 8 a is a vertical histogram of an upper case “i” shown inFIG. 6 which can be applied as inputs to an artificial neural network for training or recognition in the present invention. -
FIG. 8 b is a horizontal histogram of an upper case “i” shown inFIG. 6 which can be applied as inputs to an artificial neural network for training or recognition in the present invention. - As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure.
- Referring to the drawings in more detail, the
reference numeral 1 generally designates a process for intelligent importation of information from a user interface or raster display 2 (FIG. 1 ) of a foreign orhost application 3 using artificial intelligence. Theprocess 1 has potential for application to a number of solutions and in the present invention is used to image-enable thehost application 3 keying external files, such asimages 4, to data displayed in rasterized form by the host application. Thehost application 3 may be any of a number of software applications such as, for example, Oracle Financials (Oracle Corporation of Redwood Shores, Calif., www.oracle.com). Theprocess 1 may cooperate with a separateimage management system 5 to store and retrieve theimages 4 or may incorporate such an image management system internally. Theprocess 1 must be trained to recognize text characters or symbols and must be set up for theparticular host application 3, as will be detailed below. - The
host application 3 may be a stand-alone application or a client/server type of software. Thehost application 3, or a client component thereof, is run with theprocess 1 running in the background. Referring toFIG. 2 , at step 10 a user accesses a data record in thehost application 3. If the user needs to view a previously storedimage 4 or store an image, atstep 12 the user activates the intelligent importation (II)process 1, as by a keystroke combination or by mouse clicking an icon or button on thedisplay 2. Theprocess 1 copies current raster data from a previously designated display area of thedisplay 2 atstep 14. Atstep 16, theprocess 1 extracts character data from the raster data to form a key previously associated with a storedimage 4 or to be associated with an image to be stored by theimage management system 5. Theprocess 1 extracts the character data from the raster data using one or more artificialneural networks FIG. 3 ) as will be detailed further below. Atstep 22, theprocess 1 passes the key to theimage management system 5 which either stores animage 4 atstep 24 or retrieves and causes the display of the image atstep 26. -
FIG. 3 illustrates coreextraction processing components 28 of theintelligent importation process 1 of the present invention. Thecore components 28 include the artificial neural networks 18-20. Generally, an artificial neural network, as used in the present invention, is a system of simulated neurons defined by equations which are interconnected in such a way as to be capable of being trained to recognize various patterns of data. A pattern of raster data can be analyzed in a number of ways to thereby generate unique sets of data associated with the same raster pattern. Such sets of data for known patterns can be applied as inputs to neural networks to train the networks to recognize the patterns. Thereafter, sets of data for unknown patterns of raster data can be applied to the inputs of the networks for recognition. - The present invention includes three methods of analyzing or processing raster pattern data and provides an artificial neural network for each method. The first method is analysis of the rectangular coordinates of character pixels and binary color (black or white) of each pixel. For this a raster
neural network 18 is used.FIG. 6 illustrates araster pattern 32 of a character, such as an upper case “i”. The second processing method analyzes certain features of theraster pattern 32 and makes use of a featureneural network 19.FIGS. 8 a and 8 b respectively illustrate avertical histogram 34 and ahorizontal histogram 36 of theraster pattern 32 shown inFIG. 6 which can be input to the featureneural network 19. The third processing method is vector processing and makes use of a vectorneural network 20 to analyze vectors which describe anoutline 38 of thecharacter 40, also an upper case “i”. - The neural networks 18-20 are conventional implementations of neural networks and are of a type known as a multilayer feed forward perceptitron (or perceptron). The preferred neural networks 18-20 include one input layer, two hidden layers, and one output layer. The node or simulated neuron content of the layers of the illustrated networks 18-20 is, for example, 144, 94, 70, and 94 respectively, although the present invention is not intended to be limited to such node content. Further understanding of this general type of neural network can be obtained by reference to U.S. Pat. No. 4,918,618, which is incorporated herein by reference.
- Returning to
FIG. 3 ,raster data 44 from the previously designated display area is applied tocommon preprocessing operations 46, prior to application to the inputs of the neural networks 18-20. Generally, the purpose of thecommon preprocessing operations 46 is to segment the raster data into individual characters and to render theraster data 40 at a standard size and orientation. These common preprocessing steps 46 may include conversion to binary colors, resizing, de-skewing, skeletonization, as well as initial segmentation. More particular preprocessingoperations raster patterns 32 to derive data sets from the raster data which are appropriate and useful for each of the neural networks 18-20. The particular preprocessing operations 48-50 are different depending on which neural network 18-20 the resulting data set is to be applied to. For the rasterneural network 18, theraster preprocessing operations 48 derive the coordinates of the black pixels (marked with an “X” inFIG. 6 ) of theraster pattern 32 within a standard size pixel matrix to be applied as inputs to the rasterneural network 18. It should be noted that while the pixel matrices illustrated inFIGS. 6-8 b are 10 by 10 pixel arrays, this is for illustrative purposes only; the actual matrix sizes are that which are appropriate for each particular neural network. For the featureneural network 19, the feature preprocessing steps form the input data set for input to the feature neural network by the column and row counts of black pixels in the vertical andhorizontal histograms pattern 32. Finally, for the vectorneural network 20, the input data set is formed by the vector preprocessing steps 50 by deriving a set of Fourier coefficients, such as the first 30, resulting from a Fourier Transform applied to vectors describing anoutline 38 of thecharacter 40 formed by theraster pattern 32 or a variation thereof. - The neural networks 18-20 are trained to recognize alphanumeric characters and certain punctuation symbols. The outputs from each of the neural networks 18-20, from processing the data sets provided by their respective preprocessing steps 48-50, are a set of candidate characters, a numeric probability of each candidate character, for example three candidate characters, and a signal to noise value for each candidate character which evaluates the possible existence of extraneous pixels in the
original raster data 44. Inpost-processing steps neural networks lines 58 illustrated inFIG. 3 , using different processing criteria. One possible problem which might cause failure of the threshold tests is inaccurate segmentation. To overcome this, adjustments are made to the raster pattern segmentation processing, and a limited number of retries are made to attempt to improve the probability and signal to noise figures for the candidate characters. - Once the probability and signal to noise figures pass the threshold tests, the post-processing steps 54-56 extracts the unaltered character metrics of height, width, and distance from the previous character as well as the best candidate character, signal to noise figure and probability score. Additional post-processing of the best candidate character is used to determine the case of the candidate character from the character metrics (overall dimensions in pixels) and to distinguish ambiguous characters, such as upper case “i” (I) and lower case “L” (I), from the context of the character. The results of each of the post-processing steps 54-56 are then applied to a
voting process 60 which determines the final results 62. In summary, thedata extraction components 28 of theprocess 1 employ three effectively parallel extraction processes—raster processing 66,feature processing 67, andvector processing 68—to make their best guess at each character represented in theraster data 44, and vote on their candidate characters to determine theresults 62. Theresults 62 form a key which either is or will be associated with animage 4 in theimage management system 5. -
FIGS. 4 a, 4 b, and 5 illustrate component steps of theintelligent importation process 1 in greater detail than inFIG. 2 . Referring toFIG. 4 a, the user initiates the lookup of an image based on keys in thehost application 3 atstep 72 by accessing a screen in the application which has been previously set up, such as a data record, and activating theprocess 1 by operating a keystroke combination or clicking a particular icon. This causes theprocess 1 to extractraster data 44 from the pre-designated key region of the host application user interface (UI) ordisplay 2, atstep 14. This is done by theprocess 1 causing the reading and storing of pixel data currently stored in the previously designated coordinate range of the display buffer (not shown). The extractedraster data 44 is checked atstep 74 to determine if the raster region is rendered in color or in a grayscale. If the raster region is in color, it is converted to a 256 level grayscale atstep 76. Atstep 78, the grayscale raster data is thresholded to “binary colors”, that is, black and white, based on the assumption that the most common color is background. The overall purpose ofsteps step 80 to separate the raster data of the raster region into raster data of the individual characters displayed. - Following
segmentation 80, the raster data for each character is applied to raster, feature, and vector preprocessing steps 82, 83, and 84, as described with reference to theprocessing components 28 inFIG. 3 , to derive respective data sets describing the unknown characters within the raster region according to different graphic criteria represented by raster processing, feature processing, and vector processing. The data sets, so derived, are applied as inputs to the rasterneural network 18 atstep 88, the featureneural network 19 atstep 89, and the vectorneural network 20 atstep 90. The outputs of the neural network steps 88-90 are applied to respective post-processing steps 92 (FIG. 4 b), as described bypost-processing components FIG. 3 and including thevoting procedure 60. -
FIG. 5 describes thepost-processing steps 92 in greater detail. Atstep 100, the non-scaled height of the character is determined. This is the character height from the original raster region prior to any pre-processing. Proceeding fromstep 100, a check is made atstep 102 of the signal to noise figure for the character block to determine if the number of any extraneous pixels in the background is such as to render the character indeterminate or a “suspicious” character. If so, the character is replaced with a designated suspicious character, such as an asterisk atstep 104. If the character is not a suspicious character, an upper case verification check occurs atstep 106 by determining if the character height is within 2 pixels of the maximum height of the preprocessed characters. If not, the character is changed to a lower case version atstep 108. Thistest 106/108 is particularly important with the upper case characters: C, O, S, U, V, W, X, and Z. A somewhat similar lowercase verification check 110 determines if the character height is within 1 pixel of the maximum height. If so, the character is changed to an upper case version of the character atstep 112.Steps step 116. The post-processing steps 100-116 are all repeated atstep 118 for each character of text detected in the raster region. -
Steps ones conversion step 120, all stand-alone ones are changed to capital “i”. At an upper case “i”conversion step 122, each lower case “L” is changed to an upper case “i” if any of the following context conditions apply: if the character is the first character of a word; or if the character is surrounded by capital letters; or if the character is preceded by a space; or if the character is preceded by two capitals or a capital and a space. At a lower case “L”conversion step 124, the each upper case “i” is changed to a lower case “L” if the character is preceded by a lower case character. - Returning to
FIG. 4 b, if there are more characters to recognize in the raster region, as determined bytest 128, the recognized and post-processed character, or suspicious character, is saved atstep 130, the previously processed portion of the raster data is removed and/orsegmentation 80 is performed on the remainder of the raster region atstep 132. The results ofpost-processing 92 may indicate that theoriginal segmentation 80 was inaccurate, whereby further attempts at re-segmentation are made to attempt to find separation between sequential characters represented in the raster region. When all the raster data for a given raster region have been processed, as indicated bytest 128, the character string recognized is saved atstep 134 as a key to animage 4 in theimage management system 5. If the recognized key contains one or more suspicious or unrecognized characters, theprocess 1 preferably displays a message requiring user intervention to either verify a suggested identification of the incomplete key or to type in the character content of the designated raster region as a key. - If the accessed screen or record of the
host application 3 contains more keys populating additional areas of the screen, as determined attest 136, theprocess 1 returns to the initiation step 72 (FIG. 4 a). Otherwise, an image database of theimage management system 5 is queried atstep 138 for the image or images associated with the key or keys derived from the pre-designated raster regions and displayed atstep 140. As described in reference toFIG. 2 , images can also be associated with specific text entered in the designated regions and stored in theimage management system 5 using steps similar to those illustrated inFIGS. 4 a, 4 b, and 5. - For further understanding of some of the terms used in this invention, a glossary is provided below:
- Artificial Neural Network (ANN): A neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. Typically, a neural network is initially “trained” or fed large amounts of data and rules about data relationships (for example, “A mother is older than her daughter.”). A program can then tell the network how to behave in response to an external stimulus (for example, to input from a computer user who is interacting with the network) or can initiate activity on its own (within the limits of its access to the external world).
- In making determinations, neural networks use several principles, including gradient-based training, fuzzy logic, genetic algorithms, and Bayesian methods. Neural networks are sometimes described in terms of knowledge layers, with, in general, more complex networks having deeper layers. In feedforward systems, learned relationships about data can “feed forward” to higher layers of knowledge. Neural networks can also learn temporal concepts and have been widely used in signal processing and time series analysis.
- Bi-cubic interpolation: In numerical analysis, a branch of mathematics, bicubic interpolation is one of the most common interpolation methods in two dimensions. With this method, the value f(x, y) of a function f at a point (x, y) is computed as a weighted average of the nearest sixteen pixels in a rectangular grid (a 4×4 array).
- Binary: Binary describes a numbering scheme in which there are only two possible values for each digit: 0 and 1. The term also refers to any digital encoding/decoding system in which there are exactly two possible states. In digital data memory, storage, processing, and communications, the 0 and 1 values are sometimes called “low” and “high,” respectively.
- Fourier transforms: The Fourier transform, named after Joseph Fourier, is an integral transform that re-expresses a function in terms of sinusoidal basis functions, i.e. as a sum or integral of sinusoidal functions multiplied by some coefficients (“amplitudes”).
- Grayscale: In computing, a grayscale or greyscale digital image is an image in which the value of each pixel is a single sample. Displayed images of this sort are typically composed of shades of gray, varying from black at the weakest intensity to white at the strongest, though in principle the samples could be displayed as shades of any color, or even coded with various colors for different intensities. Grayscale images are distinct from black-and-white images, which in the context of computer imaging are images with only two colors, black and white; grayscale images have many shades of gray in between.
- Histogram: A histogram is a specialized graph or plot used in statistics. In its most common form, the independent variable is plotted along the horizontal axis, and the dependent variable (usually a percentage) is plotted along the vertical axis. The independent variable can attain only a finite number of discrete values (for example, five) rather than a continuous range of values. The dependent variable can span a continuous range.
- Raster data: Raster graphics are digital images created or captured (for example, by scanning in a photo) as a set of samples of a given space. A raster is a grid of x and y coordinates on a display space. (And for three-dimensional images, a z coordinate.) A raster image file identifies which of these coordinates to illuminate in monochrome or color values. The raster file is sometimes referred to as a bitmap because it contains information that is directly mapped to the display grid.
- Segmentation: In image analysis, segmentation is the partition of a digital image into multiple regions (sets of pixels), according to some criterion. The goal of segmentation is typically to locate certain objects of interest which may be depicted in the image. Segmentation could therefore be seen as a computer vision problem. Unfortunately, many important segmentation algorithms are too simple to solve this problem accurately: they compensate for this limitation with their predictability, generality, and efficiency.
- Vector data: Vector graphics or geometric modeling is the use of geometrical primitives such as points, lines, curves, and polygons to represent images in computer graphics. It is used by contrast to the term raster graphics, which is the representation of images as a collection of pixels (dots).
- While the present invention describes particular techniques for deriving unique sets of data describing characters represented by raster data for use as inputs to artificial neural networks for recognition of the characters, such techniques are provided herein as exemplary embodiments of the invention. However, it is also foreseen that other known techniques for deriving unique data sets exist and could be advantageously employed in the present invention. Therefore, it is to be understood that while certain forms of the present invention have been illustrated and described herein, it is not to be limited to the specific forms or arrangement of parts described and shown.
Claims (24)
Priority Applications (10)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/358,436 US7653244B2 (en) | 2005-02-22 | 2006-02-21 | Intelligent importation of information from foreign applications user interface |
AT06735723T ATE553448T1 (en) | 2005-02-22 | 2006-02-22 | INTELLIGENT IMPORT OF INFORMATION FROM THE USER INTERFACE OF A THIRD PARTY APPLICATION USING ARTIFICIAL INTELLIGENCE |
KR1020077019479A KR20070115908A (en) | 2005-02-22 | 2006-02-22 | Intellegent importation of information from foreign application user interface using artifical intellegnece |
MX2007010180A MX2007010180A (en) | 2005-02-22 | 2006-02-22 | Intelligent importation of information from foreign application user interface using artificial intelligence. |
EP06735723A EP1854051B1 (en) | 2005-02-22 | 2006-02-22 | Intelligent importation of information from foreign application user interface using artificial intelligence |
JP2007556411A JP2008537198A (en) | 2005-02-22 | 2006-02-22 | Intelligent import of information from a foreign application user interface using artificial intelligence |
PCT/US2006/006180 WO2006091626A2 (en) | 2005-02-22 | 2006-02-22 | Intelligent importation of information from foreign application user interface using artificial intelligence |
CA2598686A CA2598686C (en) | 2005-02-22 | 2006-02-22 | Intelligent importation of information from foreign application user interface using artificial intelligence |
DK06735723.6T DK1854051T3 (en) | 2005-02-22 | 2006-02-22 | INTELLIGENT IMPORTATION OF INFORMATION FROM THE THIRD PARTY APPLICATION INTERFACE USING ARTIFICIAL INTELLIGENCE |
HK08102999.9A HK1108958A1 (en) | 2005-02-22 | 2008-03-14 | Intelligent importation of information from foreign application user interface using artificial intelligence |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US65506505P | 2005-02-22 | 2005-02-22 | |
US11/358,436 US7653244B2 (en) | 2005-02-22 | 2006-02-21 | Intelligent importation of information from foreign applications user interface |
Publications (2)
Publication Number | Publication Date |
---|---|
US20070009155A1 true US20070009155A1 (en) | 2007-01-11 |
US7653244B2 US7653244B2 (en) | 2010-01-26 |
Family
ID=36927963
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/358,436 Active 2027-05-15 US7653244B2 (en) | 2005-02-22 | 2006-02-21 | Intelligent importation of information from foreign applications user interface |
Country Status (10)
Country | Link |
---|---|
US (1) | US7653244B2 (en) |
EP (1) | EP1854051B1 (en) |
JP (1) | JP2008537198A (en) |
KR (1) | KR20070115908A (en) |
AT (1) | ATE553448T1 (en) |
CA (1) | CA2598686C (en) |
DK (1) | DK1854051T3 (en) |
HK (1) | HK1108958A1 (en) |
MX (1) | MX2007010180A (en) |
WO (1) | WO2006091626A2 (en) |
Cited By (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060018544A1 (en) * | 2004-07-20 | 2006-01-26 | Yoshihisa Ohguro | Method and apparatus for detecting an orientation of characters in a document image |
WO2008133951A2 (en) * | 2007-04-24 | 2008-11-06 | Massachusetts Institute Of Technology | Method and apparatus for image processing |
US20130223721A1 (en) * | 2008-01-18 | 2013-08-29 | Mitek Systems | Systems and methods for developing and verifying image processing standards for moble deposit |
US8533182B1 (en) * | 2012-05-31 | 2013-09-10 | David P. Charboneau | Apparatuses, systems, and methods for efficient graph pattern matching and querying |
US20180225519A1 (en) * | 2017-02-03 | 2018-08-09 | Fuji Xerox Co., Ltd. | Method and system to generate targeted captions and summarize long, continuous media files |
US20180365512A1 (en) * | 2017-06-20 | 2018-12-20 | Nvidia Corporation | Equivariant landmark transformation for landmark localization |
US20190132016A1 (en) * | 2016-10-27 | 2019-05-02 | W. Jason Guzek | Mobile computing/communicating attachment device |
US10360447B2 (en) | 2013-03-15 | 2019-07-23 | Mitek Systems, Inc. | Systems and methods for assessing standards for mobile image quality |
US10558972B2 (en) | 2008-01-18 | 2020-02-11 | Mitek Systems, Inc. | Systems and methods for mobile image capture and processing of documents |
US10607073B2 (en) | 2008-01-18 | 2020-03-31 | Mitek Systems, Inc. | Systems and methods for classifying payment documents during mobile image processing |
US10685223B2 (en) | 2008-01-18 | 2020-06-16 | Mitek Systems, Inc. | Systems and methods for mobile image capture and content processing of driver's licenses |
CN113343644A (en) * | 2015-11-18 | 2021-09-03 | 谷歌有限责任公司 | Simulated hyperlinks on mobile devices |
WO2022192417A1 (en) * | 2021-03-09 | 2022-09-15 | Togal.Ai, Inc. | Methods and apparatus for artificial intelligence conversion of change orders into an actionable interface |
US11461702B2 (en) | 2018-12-04 | 2022-10-04 | Bank Of America Corporation | Method and system for fairness in artificial intelligence based decision making engines |
US11481704B2 (en) | 2021-03-09 | 2022-10-25 | Togal.Ai Inc. | Methods and apparatus for artificial intelligence conversion of change orders into an actionable interface |
US11714940B2 (en) | 2021-03-09 | 2023-08-01 | Togal.Ai Inc. | Artificial intelligence determination of building metrics for code compliance with user interaction |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100254606A1 (en) * | 2005-12-08 | 2010-10-07 | Abbyy Software Ltd | Method of recognizing text information from a vector/raster image |
US8954184B2 (en) * | 2011-01-19 | 2015-02-10 | Tokyo Electron Limited | Tool performance by linking spectroscopic information with tool operational parameters and material measurement information |
JP6515438B2 (en) * | 2014-03-27 | 2019-05-22 | 日本電気株式会社 | Importance calculation apparatus, importance calculation apparatus method, and importance calculation apparatus system |
RU2726185C1 (en) * | 2020-01-21 | 2020-07-09 | Общество с ограниченной ответстсвенностью «Аби Продакшн» | Detecting and identifying objects on images |
Citations (28)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4553206A (en) * | 1983-10-03 | 1985-11-12 | Wang Laboratories, Inc. | Image storage and retrieval |
US4817050A (en) * | 1985-11-22 | 1989-03-28 | Kabushiki Kaisha Toshiba | Database system |
US4819156A (en) * | 1986-06-13 | 1989-04-04 | International Business Machines Corporation | Database index journaling for enhanced recovery |
US4992887A (en) * | 1988-02-06 | 1991-02-12 | Dainippon Screen Mfg. Co., Ltd. | Method of storing and transmitting image data as an image file suitable for an image search |
US5022090A (en) * | 1987-09-25 | 1991-06-04 | Minolta Camera Kabushiki Kaisha | Digital image processing apparatus for correctly addressing image memory |
US5022091A (en) * | 1990-02-28 | 1991-06-04 | Hughes Aircraft Company | Image processing technique |
US5025483A (en) * | 1987-12-18 | 1991-06-18 | International Business Machines Corporation | System for scanning documents without loss of image data |
US5133024A (en) * | 1989-10-24 | 1992-07-21 | Horst Froessl | Image data bank system with selective conversion |
US5208905A (en) * | 1987-11-16 | 1993-05-04 | Canon Kabushiki Kaisha | Document processing apparatus |
US5231579A (en) * | 1989-10-20 | 1993-07-27 | Hitachi, Ltd. | Method and system for displaying and processing image of form document |
US5337370A (en) * | 1992-02-28 | 1994-08-09 | Environmental Research Institute Of Michigan | Character recognition method employing non-character recognizer |
US5406384A (en) * | 1991-06-28 | 1995-04-11 | Kabushiki Kaisha Toshiba | Document filing apparatus in which a title and/or a key word is selected from image information displayed |
US5442715A (en) * | 1992-04-06 | 1995-08-15 | Eastman Kodak Company | Method and apparatus for cursive script recognition |
US5475768A (en) * | 1993-04-29 | 1995-12-12 | Canon Inc. | High accuracy optical character recognition using neural networks with centroid dithering |
US5521985A (en) * | 1992-08-13 | 1996-05-28 | International Business Machines Corporation | Apparatus for recognizing machine generated or handprinted text |
US5586240A (en) * | 1992-03-11 | 1996-12-17 | Genesis Software, Inc. | Image generation and retrieval system integrated with arbitrary application using layered interface |
US5633954A (en) * | 1993-05-18 | 1997-05-27 | Massachusetts Institute Of Technology | System and method for character recognition with normalization |
US5745610A (en) * | 1993-07-22 | 1998-04-28 | Xerox Corporation | Data access based on human-produced images |
US5832108A (en) * | 1993-08-26 | 1998-11-03 | International Business Machines Corporation | Pattern recognition method using a network and system therefor |
US5835633A (en) * | 1995-11-20 | 1998-11-10 | International Business Machines Corporation | Concurrent two-stage multi-network optical character recognition system |
US5884296A (en) * | 1995-03-13 | 1999-03-16 | Minolta Co., Ltd. | Network and image area attribute discriminating device and method for use with said neural network |
US6128102A (en) * | 1996-01-31 | 2000-10-03 | Ricoh Company, Ltd. | Apparatus and method for filing, registering, and retrieving image files |
US6446119B1 (en) * | 1997-08-07 | 2002-09-03 | Laslo Olah | System and method for monitoring computer usage |
US20040181815A1 (en) * | 2001-11-19 | 2004-09-16 | Hull Jonathan J. | Printer with radio or television program extraction and formating |
US20050035193A1 (en) * | 1997-05-30 | 2005-02-17 | Capital Security Systems, Inc. | Automated document cashing system |
US6985643B1 (en) * | 1998-04-30 | 2006-01-10 | Anoto Group Ab | Device and method for recording hand-written information |
US7016529B2 (en) * | 2002-03-15 | 2006-03-21 | Microsoft Corporation | System and method facilitating pattern recognition |
US7085437B2 (en) * | 2000-01-20 | 2006-08-01 | Riso Kagaku Corporation | Document modification apparatus and image processing apparatus |
-
2006
- 2006-02-21 US US11/358,436 patent/US7653244B2/en active Active
- 2006-02-22 MX MX2007010180A patent/MX2007010180A/en active IP Right Grant
- 2006-02-22 KR KR1020077019479A patent/KR20070115908A/en active IP Right Grant
- 2006-02-22 AT AT06735723T patent/ATE553448T1/en active
- 2006-02-22 DK DK06735723.6T patent/DK1854051T3/en active
- 2006-02-22 EP EP06735723A patent/EP1854051B1/en active Active
- 2006-02-22 JP JP2007556411A patent/JP2008537198A/en active Pending
- 2006-02-22 CA CA2598686A patent/CA2598686C/en active Active
- 2006-02-22 WO PCT/US2006/006180 patent/WO2006091626A2/en active Application Filing
-
2008
- 2008-03-14 HK HK08102999.9A patent/HK1108958A1/en unknown
Patent Citations (29)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4553206A (en) * | 1983-10-03 | 1985-11-12 | Wang Laboratories, Inc. | Image storage and retrieval |
US4817050A (en) * | 1985-11-22 | 1989-03-28 | Kabushiki Kaisha Toshiba | Database system |
US4819156A (en) * | 1986-06-13 | 1989-04-04 | International Business Machines Corporation | Database index journaling for enhanced recovery |
US5022090A (en) * | 1987-09-25 | 1991-06-04 | Minolta Camera Kabushiki Kaisha | Digital image processing apparatus for correctly addressing image memory |
US5208905A (en) * | 1987-11-16 | 1993-05-04 | Canon Kabushiki Kaisha | Document processing apparatus |
US5025483A (en) * | 1987-12-18 | 1991-06-18 | International Business Machines Corporation | System for scanning documents without loss of image data |
US4992887A (en) * | 1988-02-06 | 1991-02-12 | Dainippon Screen Mfg. Co., Ltd. | Method of storing and transmitting image data as an image file suitable for an image search |
US5231579A (en) * | 1989-10-20 | 1993-07-27 | Hitachi, Ltd. | Method and system for displaying and processing image of form document |
US5133024A (en) * | 1989-10-24 | 1992-07-21 | Horst Froessl | Image data bank system with selective conversion |
US5022091A (en) * | 1990-02-28 | 1991-06-04 | Hughes Aircraft Company | Image processing technique |
US5406384A (en) * | 1991-06-28 | 1995-04-11 | Kabushiki Kaisha Toshiba | Document filing apparatus in which a title and/or a key word is selected from image information displayed |
US5337370A (en) * | 1992-02-28 | 1994-08-09 | Environmental Research Institute Of Michigan | Character recognition method employing non-character recognizer |
US5586240A (en) * | 1992-03-11 | 1996-12-17 | Genesis Software, Inc. | Image generation and retrieval system integrated with arbitrary application using layered interface |
US5442715A (en) * | 1992-04-06 | 1995-08-15 | Eastman Kodak Company | Method and apparatus for cursive script recognition |
US5521985A (en) * | 1992-08-13 | 1996-05-28 | International Business Machines Corporation | Apparatus for recognizing machine generated or handprinted text |
US5475768A (en) * | 1993-04-29 | 1995-12-12 | Canon Inc. | High accuracy optical character recognition using neural networks with centroid dithering |
US5633954A (en) * | 1993-05-18 | 1997-05-27 | Massachusetts Institute Of Technology | System and method for character recognition with normalization |
US5745610A (en) * | 1993-07-22 | 1998-04-28 | Xerox Corporation | Data access based on human-produced images |
US5832108A (en) * | 1993-08-26 | 1998-11-03 | International Business Machines Corporation | Pattern recognition method using a network and system therefor |
US5884296A (en) * | 1995-03-13 | 1999-03-16 | Minolta Co., Ltd. | Network and image area attribute discriminating device and method for use with said neural network |
US5835633A (en) * | 1995-11-20 | 1998-11-10 | International Business Machines Corporation | Concurrent two-stage multi-network optical character recognition system |
US6128102A (en) * | 1996-01-31 | 2000-10-03 | Ricoh Company, Ltd. | Apparatus and method for filing, registering, and retrieving image files |
US20050035193A1 (en) * | 1997-05-30 | 2005-02-17 | Capital Security Systems, Inc. | Automated document cashing system |
US6446119B1 (en) * | 1997-08-07 | 2002-09-03 | Laslo Olah | System and method for monitoring computer usage |
US6985643B1 (en) * | 1998-04-30 | 2006-01-10 | Anoto Group Ab | Device and method for recording hand-written information |
US7085437B2 (en) * | 2000-01-20 | 2006-08-01 | Riso Kagaku Corporation | Document modification apparatus and image processing apparatus |
US20040181815A1 (en) * | 2001-11-19 | 2004-09-16 | Hull Jonathan J. | Printer with radio or television program extraction and formating |
US7016529B2 (en) * | 2002-03-15 | 2006-03-21 | Microsoft Corporation | System and method facilitating pattern recognition |
US7286699B2 (en) * | 2002-03-15 | 2007-10-23 | Microsoft Corporation | System and method facilitating pattern recognition |
Cited By (29)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7580571B2 (en) * | 2004-07-20 | 2009-08-25 | Ricoh Company, Ltd. | Method and apparatus for detecting an orientation of characters in a document image |
US20060018544A1 (en) * | 2004-07-20 | 2006-01-26 | Yoshihisa Ohguro | Method and apparatus for detecting an orientation of characters in a document image |
US9799098B2 (en) | 2007-04-24 | 2017-10-24 | Massachusetts Institute Of Technology | Method and apparatus for image processing |
WO2008133951A2 (en) * | 2007-04-24 | 2008-11-06 | Massachusetts Institute Of Technology | Method and apparatus for image processing |
WO2008133951A3 (en) * | 2007-04-24 | 2008-12-31 | Massachusetts Inst Technology | Method and apparatus for image processing |
US10558972B2 (en) | 2008-01-18 | 2020-02-11 | Mitek Systems, Inc. | Systems and methods for mobile image capture and processing of documents |
US10685223B2 (en) | 2008-01-18 | 2020-06-16 | Mitek Systems, Inc. | Systems and methods for mobile image capture and content processing of driver's licenses |
US8983170B2 (en) * | 2008-01-18 | 2015-03-17 | Mitek Systems, Inc. | Systems and methods for developing and verifying image processing standards for mobile deposit |
US10192108B2 (en) * | 2008-01-18 | 2019-01-29 | Mitek Systems, Inc. | Systems and methods for developing and verifying image processing standards for mobile deposit |
US20210103723A1 (en) * | 2008-01-18 | 2021-04-08 | Mitek Systems, Inc. | Systems and methods for developing and verifying image processing standards for mobile deposit |
US20190228222A1 (en) * | 2008-01-18 | 2019-07-25 | Mitek Systems, Inc. | Systems and methods for developing and verifying image processing standards for mobile deposit |
US20130223721A1 (en) * | 2008-01-18 | 2013-08-29 | Mitek Systems | Systems and methods for developing and verifying image processing standards for moble deposit |
US10909362B2 (en) * | 2008-01-18 | 2021-02-02 | Mitek Systems, Inc. | Systems and methods for developing and verifying image processing standards for mobile deposit |
US10607073B2 (en) | 2008-01-18 | 2020-03-31 | Mitek Systems, Inc. | Systems and methods for classifying payment documents during mobile image processing |
US8533182B1 (en) * | 2012-05-31 | 2013-09-10 | David P. Charboneau | Apparatuses, systems, and methods for efficient graph pattern matching and querying |
US11157731B2 (en) | 2013-03-15 | 2021-10-26 | Mitek Systems, Inc. | Systems and methods for assessing standards for mobile image quality |
US10360447B2 (en) | 2013-03-15 | 2019-07-23 | Mitek Systems, Inc. | Systems and methods for assessing standards for mobile image quality |
CN113343644A (en) * | 2015-11-18 | 2021-09-03 | 谷歌有限责任公司 | Simulated hyperlinks on mobile devices |
US20190132016A1 (en) * | 2016-10-27 | 2019-05-02 | W. Jason Guzek | Mobile computing/communicating attachment device |
US10592751B2 (en) * | 2017-02-03 | 2020-03-17 | Fuji Xerox Co., Ltd. | Method and system to generate targeted captions and summarize long, continuous media files |
US20180225519A1 (en) * | 2017-02-03 | 2018-08-09 | Fuji Xerox Co., Ltd. | Method and system to generate targeted captions and summarize long, continuous media files |
US10783394B2 (en) * | 2017-06-20 | 2020-09-22 | Nvidia Corporation | Equivariant landmark transformation for landmark localization |
US20180365512A1 (en) * | 2017-06-20 | 2018-12-20 | Nvidia Corporation | Equivariant landmark transformation for landmark localization |
US11461702B2 (en) | 2018-12-04 | 2022-10-04 | Bank Of America Corporation | Method and system for fairness in artificial intelligence based decision making engines |
WO2022192417A1 (en) * | 2021-03-09 | 2022-09-15 | Togal.Ai, Inc. | Methods and apparatus for artificial intelligence conversion of change orders into an actionable interface |
US11475174B2 (en) | 2021-03-09 | 2022-10-18 | Togal.Ai Inc. | Methods and apparatus for artificial intelligence conversion of a two-dimensional reference into an actionable interface |
US11481704B2 (en) | 2021-03-09 | 2022-10-25 | Togal.Ai Inc. | Methods and apparatus for artificial intelligence conversion of change orders into an actionable interface |
US11714940B2 (en) | 2021-03-09 | 2023-08-01 | Togal.Ai Inc. | Artificial intelligence determination of building metrics for code compliance with user interaction |
US11797733B2 (en) | 2021-03-09 | 2023-10-24 | Togal.Ai Inc | Artificial intelligence determination of building metrics for code compliance |
Also Published As
Publication number | Publication date |
---|---|
CA2598686C (en) | 2014-12-09 |
ATE553448T1 (en) | 2012-04-15 |
DK1854051T3 (en) | 2012-07-16 |
EP1854051A4 (en) | 2010-07-14 |
KR20070115908A (en) | 2007-12-06 |
US7653244B2 (en) | 2010-01-26 |
MX2007010180A (en) | 2008-01-11 |
HK1108958A1 (en) | 2008-05-23 |
CA2598686A1 (en) | 2006-08-31 |
WO2006091626A2 (en) | 2006-08-31 |
JP2008537198A (en) | 2008-09-11 |
WO2006091626A3 (en) | 2009-04-23 |
EP1854051B1 (en) | 2012-04-11 |
EP1854051A2 (en) | 2007-11-14 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US7653244B2 (en) | Intelligent importation of information from foreign applications user interface | |
US11176443B1 (en) | Application control and text detection from application screen images | |
RU2699687C1 (en) | Detecting text fields using neural networks | |
CN107194400B (en) | Financial reimbursement full ticket image recognition processing method | |
RU2661750C1 (en) | Symbols recognition with the use of artificial intelligence | |
US20190294921A1 (en) | Field identification in an image using artificial intelligence | |
WO2007070662A1 (en) | Script recognition for ink notes | |
JP2016206837A (en) | Object detection method and image search system | |
CN114120349A (en) | Test paper identification method and system based on deep learning | |
Akinbade et al. | An adaptive thresholding algorithm-based optical character recognition system for information extraction in complex images | |
Gupta et al. | Machine learning tensor flow based platform for recognition of hand written text | |
CN114581928A (en) | Form identification method and system | |
US11436852B2 (en) | Document information extraction for computer manipulation | |
Sharma et al. | Primitive feature-based optical character recognition of the Devanagari script | |
Raj et al. | Grantha script recognition from ancient palm leaves using histogram of orientation shape context | |
Choudhary et al. | A neural approach to cursive handwritten character recognition using features extracted from binarization technique | |
Kataria et al. | CNN-bidirectional LSTM based optical character recognition of Sanskrit manuscripts: A comprehensive systematic literature review | |
Joshi et al. | Combination of multiple image features along with KNN classifier for classification of Marathi Barakhadi | |
Chooi et al. | Handwritten character recognition using convolutional neural network | |
Salagar et al. | Analysis of PCA usage to detect and correct skew in document images | |
Evangelou et al. | PU learning-based recognition of structural elements in architectural floor plans | |
Mironovsky et al. | Graphological analysis and identification of handwritten texts | |
KR20190085584A (en) | Text mapping system with associated of cell phone image using artifical intellegnece | |
Kashevnik et al. | An Approach to Engineering Drawing Organization: Title Block Detection and Processing | |
Sarnacki et al. | Character Recognition Based on Skeleton Analysis |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: PERCEPTIVE SOFTWARE, INC., KANSAS Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:POTTS, WESLEY F.;ANDERSON, BRIAN G.;ROGERS, JASON L.;AND OTHERS;REEL/FRAME:020290/0465;SIGNING DATES FROM 20060413 TO 20060831 |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
AS | Assignment |
Owner name: LEXMARK INTERNATIONAL TECHNOLOGY SA, SWITZERLAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:PERCEPTIVE SOFTWARE, INC.;REEL/FRAME:028488/0704 Effective date: 20100920 |
|
FPAY | Fee payment |
Year of fee payment: 4 |
|
AS | Assignment |
Owner name: LEXMARK INTERNATIONAL TECHNOLOGY SARL, SWITZERLAND Free format text: ENTITY CONVERSION;ASSIGNOR:LEXMARK INTERNATIONAL TECHNOLOGY S.A.;REEL/FRAME:037793/0300 Effective date: 20151210 |
|
FEPP | Fee payment procedure |
Free format text: PAT HOLDER NO LONGER CLAIMS SMALL ENTITY STATUS, ENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: STOL); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
FPAY | Fee payment |
Year of fee payment: 8 |
|
AS | Assignment |
Owner name: KOFAX INTERNATIONAL SWITZERLAND SARL, SWITZERLAND Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:LEXMARK INTERNATIONAL TECHNOLOGY SARL;REEL/FRAME:042919/0841 Effective date: 20170519 |
|
AS | Assignment |
Owner name: CREDIT SUISSE, NEW YORK Free format text: INTELLECTUAL PROPERTY SECURITY AGREEMENT SUPPLEMENT (FIRST LIEN);ASSIGNOR:KOFAX INTERNATIONAL SWITZERLAND SARL;REEL/FRAME:045430/0405 Effective date: 20180221 Owner name: CREDIT SUISSE, NEW YORK Free format text: INTELLECTUAL PROPERTY SECURITY AGREEMENT SUPPLEMENT (SECOND LIEN);ASSIGNOR:KOFAX INTERNATIONAL SWITZERLAND SARL;REEL/FRAME:045430/0593 Effective date: 20180221 |
|
AS | Assignment |
Owner name: HYLAND SWITZERLAND SARL, SWITZERLAND Free format text: CHANGE OF NAME;ASSIGNOR:KOFAX INTERNATIONAL SWITZERLAND SARL;REEL/FRAME:048389/0380 Effective date: 20180515 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 12TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1553); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 12 |
|
AS | Assignment |
Owner name: KOFAX INTERNATIONAL SWITZERLAND SARL, SWITZERLAND Free format text: RELEASE OF SECURITY INTEREST RECORDED AT REEL/FRAME 045430/0405;ASSIGNOR:CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS COLLATERAL AGENT, A BRANCH OF CREDIT SUISSE;REEL/FRAME:065018/0421 Effective date: 20230919 Owner name: KOFAX INTERNATIONAL SWITZERLAND SARL, SWITZERLAND Free format text: RELEASE OF SECURITY INTEREST RECORDED AT REEL/FRAME 045430/0593;ASSIGNOR:CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS COLLATERAL AGENT, A BRANCH OF CREDIT SUISSE;REEL/FRAME:065020/0806 Effective date: 20230919 |
|
AS | Assignment |
Owner name: GOLUB CAPITAL MARKETS LLC, AS COLLATERAL AGENT, ILLINOIS Free format text: SECURITY INTEREST;ASSIGNOR:HYLAND SWITZERLAND SARL;REEL/FRAME:066339/0304 Effective date: 20240116 |